Data modeling enabled dynamical analysis for blogger state-of-mind modeling and prediction

Holger M. Jaenisch; Michael J. Coombs; James W. Handley; Nathaniel G. Albritton; Matthew E. Edwards

doi:10.1117/12.775565

1 May 2008 Data modeling enabled dynamical analysis for blogger state-of-mind modeling and prediction

Holger M. Jaenisch, Michael J. Coombs, James W. Handley, Nathaniel G. Albritton, Matthew E. Edwards

Proceedings Volume 6964, Evolutionary and Bio-Inspired Computation: Theory and Applications II; 69640A (2008) https://doi.org/10.1117/12.775565
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

We present a novel mathematical framework for Data Mining blogger text entries and converting latent conceptual information into analytical predictive equations. These differential equations are conceptual models of the blogger's topic and state-of-mind transition dynamics. The mathematical framework is explored for its value in characterization of topic content and topic tracking as well as identification and prediction of topic dynamic changes.

Citation Download Citation

Holger M. Jaenisch, Michael J. Coombs, James W. Handley, Nathaniel G. Albritton, and Matthew E. Edwards "Data modeling enabled dynamical analysis for blogger state-of-mind modeling and prediction", Proc. SPIE 6964, Evolutionary and Bio-Inspired Computation: Theory and Applications II, 69640A (1 May 2008); https://doi.org/10.1117/12.775565

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